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Joint Node Selection and Resource Allocation Optimization for Cooperative Sensing with a Shared Wireless Backhaul

Mingxin Chen, Ming-Min Zhao, An Liu, Min Li, Qingjiang Shi

TL;DR

This work addresses cooperative sensing with limited wireless backhaul by proposing a hybrid information-signal domain cooperative sensing (HISDCS) framework that fuses information-domain estimates with selectively quantized signal-domain samples via a constrained backhaul. It jointly optimizes backhaul resource allocation and cooperative node selection under CRLB sensing bounds and Gaussian MAC capacity, via a matrix-inequality constrained successive convex approximation (MCSCA) algorithm and a greedy node-selection strategy, plus a low-complexity bit-reallocation option. The authors prove convergence of the MCSCA method to KKT solutions of a relaxed problem and demonstrate substantial performance gains over IDCS and SDCS baselines in simulations, especially in terms of reduced channel uses and improved localization accuracy. The approach provides a practical mechanism to trade off sensing accuracy and communication overhead in future 6G-like networks, with potential extensions to multi-target detection, MIMO integration, and more realistic channel scenarios.

Abstract

In this paper, we consider a cooperative sensing framework in the context of future multi-functional network with both communication and sensing ability, where one base station (BS) serves as a sensing transmitter and several nearby BSs serve as sensing receivers. Each receiver receives the sensing signal reflected by the target and communicates with the fusion center (FC) through a wireless multiple access channel (MAC) for cooperative target localization. To improve the localization performance, we present a hybrid information-signal domain cooperative sensing (HISDCS) design, where each sensing receiver transmits both the estimated time delay/effective reflecting coefficient and the received sensing signal sampled around the estimated time delay to the FC. Then, we propose to minimize the number of channel uses by utilizing an efficient Karhunen-Loéve transformation (KLT) encoding scheme for signal quantization and proper node selection, under the Cramér-Rao lower bound (CRLB) constraint and the capacity limits of MAC. A novel matrix-inequality constrained successive convex approximation (MCSCA) algorithm is proposed to optimize the wireless backhaul resource allocation, together with a greedy strategy for node selection. Despite the high non-convexness of the considered problem, we prove that the proposed MCSCA algorithm is able to converge to the set of Karush-Kuhn-Tucker (KKT) solutions of a relaxed problem obtained by relaxing the discrete variables. Besides, a low-complexity quantization bit reallocation algorithm is designed, which does not perform explicit node selection, and is able to harvest most of the performance gain brought by HISDCS. Finally, numerical simulations are presented to show that the proposed HISDCS design is able to significantly outperform the baseline schemes.

Joint Node Selection and Resource Allocation Optimization for Cooperative Sensing with a Shared Wireless Backhaul

TL;DR

This work addresses cooperative sensing with limited wireless backhaul by proposing a hybrid information-signal domain cooperative sensing (HISDCS) framework that fuses information-domain estimates with selectively quantized signal-domain samples via a constrained backhaul. It jointly optimizes backhaul resource allocation and cooperative node selection under CRLB sensing bounds and Gaussian MAC capacity, via a matrix-inequality constrained successive convex approximation (MCSCA) algorithm and a greedy node-selection strategy, plus a low-complexity bit-reallocation option. The authors prove convergence of the MCSCA method to KKT solutions of a relaxed problem and demonstrate substantial performance gains over IDCS and SDCS baselines in simulations, especially in terms of reduced channel uses and improved localization accuracy. The approach provides a practical mechanism to trade off sensing accuracy and communication overhead in future 6G-like networks, with potential extensions to multi-target detection, MIMO integration, and more realistic channel scenarios.

Abstract

In this paper, we consider a cooperative sensing framework in the context of future multi-functional network with both communication and sensing ability, where one base station (BS) serves as a sensing transmitter and several nearby BSs serve as sensing receivers. Each receiver receives the sensing signal reflected by the target and communicates with the fusion center (FC) through a wireless multiple access channel (MAC) for cooperative target localization. To improve the localization performance, we present a hybrid information-signal domain cooperative sensing (HISDCS) design, where each sensing receiver transmits both the estimated time delay/effective reflecting coefficient and the received sensing signal sampled around the estimated time delay to the FC. Then, we propose to minimize the number of channel uses by utilizing an efficient Karhunen-Loéve transformation (KLT) encoding scheme for signal quantization and proper node selection, under the Cramér-Rao lower bound (CRLB) constraint and the capacity limits of MAC. A novel matrix-inequality constrained successive convex approximation (MCSCA) algorithm is proposed to optimize the wireless backhaul resource allocation, together with a greedy strategy for node selection. Despite the high non-convexness of the considered problem, we prove that the proposed MCSCA algorithm is able to converge to the set of Karush-Kuhn-Tucker (KKT) solutions of a relaxed problem obtained by relaxing the discrete variables. Besides, a low-complexity quantization bit reallocation algorithm is designed, which does not perform explicit node selection, and is able to harvest most of the performance gain brought by HISDCS. Finally, numerical simulations are presented to show that the proposed HISDCS design is able to significantly outperform the baseline schemes.
Paper Structure (15 sections, 4 theorems, 71 equations, 12 figures, 3 algorithms)

This paper contains 15 sections, 4 theorems, 71 equations, 12 figures, 3 algorithms.

Table of Contents

  1. Introduction
  2. System Model
  3. Hybrid Information-Signal Domain Cooperative Sensing
  4. ML Estimation of Individual Delay and Effective Reflecting Coefficient
  5. Received Signal Quantization at Each Receiver
  6. Target Localization at the FC
  7. Problem Formulation and Algorithm Design
  8. Optimization Problem Formulation
  9. Algorithm Design
  10. MCSCA Algorithm for Quantization Bit Allocation
  11. Convergence Analysis for the MCSCA Algorithm
  12. Node Selection Strategy
  13. Low-Complexity Bit Reallocation Algorithm
  14. Simulation Results
  15. Conclusion, Challenges and Future Work

Key Result

Lemma 1

Consider the following optimization problem which is obtained by removing the constraint eq39c from problem eq39: Let $\{\boldsymbol{x}^{t}\}^{\infty }_{t=1}$ and $\{\bar{\boldsymbol{x}}^{t}\}^{\infty }_{t=1}$ denote the sequences of iterates generated by the proposed MCSCA algorithm when solving problems eq39 and eq40, respectively, then we have

Figures (12)

  • Figure 1: Cooperative sensing system model.
  • Figure 2: Illustration of the proposed design.
  • Figure 3: Procedure of the proposed ML algorithm.
  • Figure 4: Convergence behavior of the proposed MCSCA algorithm.
  • Figure 5: Comparison between the proposed and baseline designs under different SNR regimes.
  • ...and 7 more figures

Theorems & Definitions (8)

  • Remark 1
  • Remark 2
  • Lemma 1
  • proof
  • Lemma 2
  • Theorem 1
  • proof
  • Theorem 2: m-User Multiple-Access Channel Capacity